{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c3a5e62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch    : 1.12.1\n",
      "lightning: 2022.9.15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,lightning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "292759a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightning as L\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torchmetrics\n",
    "from lightning.pytorch.loggers import CSVLogger\n",
    "\n",
    "from shared_utilities import CustomDataModule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef478510-97a2-497c-8831-bb594b753d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6a6eb624-c936-4def-afee-e1d785584ffb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "lrs = []\n",
    "\n",
    "\n",
    "for i in range(num_epochs):\n",
    "    lrs.append(0.1)\n",
    "\n",
    "plt.ylabel(\"Learning rate\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.plot(lrs)\n",
    "plt.savefig('1-baseline_lr.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce808943-a20e-44d2-9303-c897226b37df",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PyTorchMLP(torch.nn.Module):\n",
    "    def __init__(self, num_features, num_classes):\n",
    "        super().__init__()\n",
    "\n",
    "        self.all_layers = torch.nn.Sequential(\n",
    "            # 1st hidden layer\n",
    "            torch.nn.Linear(num_features, 100),\n",
    "            torch.nn.BatchNorm1d(100),\n",
    "            torch.nn.ReLU(),\n",
    "            \n",
    "            # 2nd hidden layer\n",
    "            torch.nn.Linear(100, 50),\n",
    "            torch.nn.BatchNorm1d(50),\n",
    "            torch.nn.ReLU(),\n",
    "            \n",
    "            # output layer\n",
    "            torch.nn.Linear(50, num_classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.flatten(x, start_dim=1)\n",
    "        logits = self.all_layers(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59a6a00d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LightningModel(L.LightningModule):\n",
    "    def __init__(self, model, learning_rate, cosine_t_max):\n",
    "        super().__init__()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        self.cosine_t_max = cosine_t_max\n",
    "        self.model = model\n",
    "\n",
    "        self.save_hyperparameters(ignore=[\"model\"])\n",
    "\n",
    "        self.train_acc = torchmetrics.Accuracy()\n",
    "        self.val_acc = torchmetrics.Accuracy()\n",
    "        self.test_acc = torchmetrics.Accuracy()\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def _shared_step(self, batch):\n",
    "        features, true_labels = batch\n",
    "        logits = self(features)\n",
    "\n",
    "        loss = F.cross_entropy(logits, true_labels)\n",
    "        predicted_labels = torch.argmax(logits, dim=1)\n",
    "        return loss, true_labels, predicted_labels\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "\n",
    "        self.log(\"train_loss\", loss)\n",
    "        self.train_acc(predicted_labels, true_labels)\n",
    "        self.log(\n",
    "            \"train_acc\", self.train_acc, prog_bar=True, on_epoch=True, on_step=False\n",
    "        )\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "\n",
    "        self.log(\"val_loss\", loss, prog_bar=True)\n",
    "        self.val_acc(predicted_labels, true_labels)\n",
    "        self.log(\"val_acc\", self.val_acc, prog_bar=True)\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.test_acc(predicted_labels, true_labels)\n",
    "        self.log(\"test_acc\", self.test_acc)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        opt = torch.optim.SGD(self.parameters(), lr=self.learning_rate)\n",
    "        sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=self.cosine_t_max)\n",
    "\n",
    "\n",
    "        return {\n",
    "            \"optimizer\": opt,\n",
    "            \"lr_scheduler\": {\n",
    "                \"scheduler\": sch,\n",
    "                \"monitor\": \"train_loss\",\n",
    "                \"interval\": \"epoch\", # step means \"batch\" here, default: epoch\n",
    "                \"frequency\": 1, # default\n",
    "            },\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bbe23398-50d6-4ed6-8ce5-4d2e18d38a25",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightning.pytorch.callbacks import ModelCheckpoint\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(save_top_k=1, mode=\"max\", monitor=\"val_acc\", save_last=True)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3e70623",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 123\n",
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f603c5eb4cfc40be8b59406977f98ead",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Finding best initial lr:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_steps=100` reached.\n",
      "Learning rate set to 0.15848931924611143\n",
      "Restoring states from the checkpoint path at /Users/sebastianraschka/Desktop/cosine/.lr_find_953e439c-618a-4d8a-a360-87db33e9100a.ckpt\n"
     ]
    }
   ],
   "source": [
    "%%capture --no-display\n",
    "\n",
    "L.seed_everything(123)\n",
    "dm = CustomDataModule()\n",
    "\n",
    "pytorch_model = PyTorchMLP(num_features=100, num_classes=2)\n",
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.1, cosine_t_max=1)\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    max_epochs=num_epochs,\n",
    "    auto_lr_find=True,\n",
    "    accelerator=\"cpu\",\n",
    "    devices=\"auto\",\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"my-model\"),\n",
    "    deterministic=True,\n",
    "    callbacks=callbacks,\n",
    ")\n",
    "\n",
    "results = trainer.tune(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0542570a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cb/1y0v6fzx5c54sztp5nqq39jc0000gn/T/ipykernel_76497/735961897.py:3: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = results[\"lr_find\"].plot(suggest=True)\n",
    "# fig.savefig(\"lr_suggest.pdf\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e9293189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15848931924611143\n"
     ]
    }
   ],
   "source": [
    "# get suggestion\n",
    "new_lr = results[\"lr_find\"].suggestion()\n",
    "print(new_lr)\n",
    "\n",
    "# update hparams of the model\n",
    "lightning_model.hparams.learning_rate = new_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eb1581b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name      | Type       | Params\n",
      "-----------------------------------------\n",
      "0 | model     | PyTorchMLP | 15.6 K\n",
      "1 | train_acc | Accuracy   | 0     \n",
      "2 | val_acc   | Accuracy   | 0     \n",
      "3 | test_acc  | Accuracy   | 0     \n",
      "-----------------------------------------\n",
      "15.6 K    Trainable params\n",
      "0         Non-trainable params\n",
      "15.6 K    Total params\n",
      "0.062     Total estimated model params size (MB)\n"
     ]
    },
    {
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       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastianraschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:231: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastianraschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:231: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
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      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32931e75d2e342ba94101d1a61419ef2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e993cdedd94b47c08782741f827638f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0bfe66d3a40f47079e991d44b749689a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9c0a9da48694e2e949429a60f5fa4b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1f379b9c5634c71b844fddcc6652a00",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=200` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer.fit(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c6fa764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
    "\n",
    "aggreg_metrics = []\n",
    "agg_col = \"epoch\"\n",
    "for i, dfg in metrics.groupby(agg_col):\n",
    "    agg = dict(dfg.mean())\n",
    "    agg[agg_col] = i\n",
    "    aggreg_metrics.append(agg)\n",
    "\n",
    "df_metrics = pd.DataFrame(aggreg_metrics)\n",
    "df_metrics[[\"train_loss\", \"val_loss\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"Loss\"\n",
    ")\n",
    "\n",
    "\n",
    "df_metrics[[\"train_acc\", \"val_acc\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n",
    ")\n",
    "\n",
    "plt.ylim([0.7, 1.0])\n",
    "\n",
    "plt.savefig('1-baseline_acc.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0df61ef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at logs/my-model/version_17/checkpoints/epoch=144-step=65250.ckpt\n",
      "Loaded model weights from checkpoint at logs/my-model/version_17/checkpoints/epoch=144-step=65250.ckpt\n",
      "/Users/sebastianraschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:231: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
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       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8892499804496765     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
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       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
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       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8892499804496765    \u001b[0m\u001b[35m \u001b[0m│\n",
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   "execution_count": 13,
   "id": "1c17a3c0",
   "metadata": {},
   "outputs": [
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     "text": [
      "Restoring states from the checkpoint path at logs/my-model/version_17/checkpoints/last.ckpt\n",
      "Loaded model weights from checkpoint at logs/my-model/version_17/checkpoints/last.ckpt\n"
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       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8852499723434448     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
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       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
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       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8852499723434448    \u001b[0m\u001b[35m \u001b[0m│\n",
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